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dc.contributor.authorDagher, Issamen_US
dc.contributor.authorGeorgiopoulos, Men_US
dc.contributor.authorHeileman, G.Len_US
dc.contributor.authorBebis, Gen_US
dc.description.abstractWe introduce a variation of the performance phase of fuzzy ARTMAP which is called Fuzzy ARTVar. Experimental results have shown that Fuzzy ARTVar exhibits superior generalization performance, compared to fuzzy ARTMAP, for a variety of machine learning databases. Furthermore, experimental results have also demonstrated that Fuzzy ARTVar compares favourably with other existing variations of fuzzy ARTMAP, such as ARTEMAP (power rule), ARTEMAPQ (Q-max rule), and Gaussian ARTMAP. The performance of Fuzzy ARTVar is independent of the tuning of network parameters, which is in contrast with the ARTEMAP, ARTEMAPQ, and Gaussian ARTMAP algorithms, whose performance depends on the choice of certain network parameters.en_US
dc.format.extent6 p.en_US
dc.subjectGeneralisation (artificial intelligence),en_US
dc.subjectFuzzy neural netsen_US
dc.subjectART neural netsen_US
dc.subjectPattern classificationen_US
dc.subjectLearning (artificial intelligence)en_US
dc.titleFuzzy ARTVar : an improved fuzzy ARTMAP algorithmen_US
dc.typeConference Paperen_US
dc.relation.conferenceIEEE International Joint Conference on Neural Networks (4-9 May 1998 : Anchorage, AK, USA)en_US
dc.contributor.affiliationDepartment of Computer Engineeringen_US
dc.relation.ispartoftextIEEE World Congress on Computational Intelligence. IEEE International Joint Conference on Neural Networks Proceedingsen_US
dc.provenance.recordsourceOliben_US of Engineering-
Appears in Collections:Department of Computer Engineering
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